The network is imbalanced, as being an influencer is less common than being a non-influencer (imbalance ratio is \({\sim }13\%\) ). We check if oversampling helps to handle the data imbalance during the model validation step and apply the Synthetic Minority Oversampling TEchnique (SMOTE) [1]} to the embeddings generated by the RNN model. We follow the oversampling strategy of [2]} and oversample the nodes in the embeddings generated by the RNN model or by the GNN encoder in static GNN models. We generate synthetic nodes of the minority class of different quantities that we set as a hyperparameter (Appendix , Table REF ).
